{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d03f124e",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# 锚框\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a0480e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:45.135226Z",
     "iopub.status.busy": "2022-07-31T22:40:45.134648Z",
     "iopub.status.idle": "2022-07-31T22:40:47.028169Z",
     "shell.execute_reply": "2022-07-31T22:40:47.027462Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "from d2l import torch as d2l\n",
    "\n",
    "torch.set_printoptions(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c753b85c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "锚框的宽度和高度分别是$ws\\sqrt{r}$和$hs/\\sqrt{r}$。\n",
    "我们只考虑\n",
    "组合：\n",
    "\n",
    "$$(s_1, r_1), (s_1, r_2), \\ldots, (s_1, r_m), (s_2, r_1), (s_3, r_1), \\ldots, (s_n, r_1)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3bd119b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.031839Z",
     "iopub.status.busy": "2022-07-31T22:40:47.031446Z",
     "iopub.status.idle": "2022-07-31T22:40:47.040792Z",
     "shell.execute_reply": "2022-07-31T22:40:47.040184Z"
    },
    "origin_pos": 5,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def multibox_prior(data, sizes, ratios):\n",
    "    \"\"\"生成以每个像素为中心具有不同形状的锚框\"\"\"\n",
    "    in_height, in_width = data.shape[-2:]\n",
    "    device, num_sizes, num_ratios = data.device, len(sizes), len(ratios)\n",
    "    boxes_per_pixel = (num_sizes + num_ratios - 1)\n",
    "    size_tensor = torch.tensor(sizes, device=device)\n",
    "    ratio_tensor = torch.tensor(ratios, device=device)\n",
    "\n",
    "    offset_h, offset_w = 0.5, 0.5\n",
    "    steps_h = 1.0 / in_height\n",
    "    steps_w = 1.0 / in_width\n",
    "\n",
    "    center_h = (torch.arange(in_height, device=device) + offset_h) * steps_h\n",
    "    center_w = (torch.arange(in_width, device=device) + offset_w) * steps_w\n",
    "    shift_y, shift_x = torch.meshgrid(center_h, center_w, indexing='ij')\n",
    "    shift_y, shift_x = shift_y.reshape(-1), shift_x.reshape(-1)\n",
    "\n",
    "    w = torch.cat((size_tensor * torch.sqrt(ratio_tensor[0]),\n",
    "                   sizes[0] * torch.sqrt(ratio_tensor[1:])))\\\n",
    "                   * in_height / in_width\n",
    "    h = torch.cat((size_tensor / torch.sqrt(ratio_tensor[0]),\n",
    "                   sizes[0] / torch.sqrt(ratio_tensor[1:])))\n",
    "    anchor_manipulations = torch.stack((-w, -h, w, h)).T.repeat(\n",
    "                                        in_height * in_width, 1) / 2\n",
    "\n",
    "    out_grid = torch.stack([shift_x, shift_y, shift_x, shift_y],\n",
    "                dim=1).repeat_interleave(boxes_per_pixel, dim=0)\n",
    "    output = out_grid + anchor_manipulations\n",
    "    return output.unsqueeze(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8310b063",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "返回的锚框变量`Y`的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3228ff2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.043666Z",
     "iopub.status.busy": "2022-07-31T22:40:47.043321Z",
     "iopub.status.idle": "2022-07-31T22:40:47.100282Z",
     "shell.execute_reply": "2022-07-31T22:40:47.099611Z"
    },
    "origin_pos": 8,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "561 728\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2042040, 4])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = d2l.plt.imread('../img/catdog.jpg')\n",
    "h, w = img.shape[:2]\n",
    "\n",
    "print(h, w)\n",
    "X = torch.rand(size=(1, 3, h, w))\n",
    "Y = multibox_prior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca1441a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "访问以（250,250）为中心的第一个锚框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e887ad8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.103633Z",
     "iopub.status.busy": "2022-07-31T22:40:47.103151Z",
     "iopub.status.idle": "2022-07-31T22:40:47.108654Z",
     "shell.execute_reply": "2022-07-31T22:40:47.108047Z"
    },
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.06, 0.07, 0.63, 0.82])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxes = Y.reshape(h, w, 5, 4)\n",
    "boxes[250, 250, 0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6380108a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "显示以图像中以某个像素为中心的所有锚框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9da779bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.121150Z",
     "iopub.status.busy": "2022-07-31T22:40:47.120634Z",
     "iopub.status.idle": "2022-07-31T22:40:47.388700Z",
     "shell.execute_reply": "2022-07-31T22:40:47.388033Z"
    },
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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    "def show_bboxes(axes, bboxes, labels=None, colors=None):\n",
    "    \"\"\"显示所有边界框\"\"\"\n",
    "    def _make_list(obj, default_values=None):\n",
    "        if obj is None:\n",
    "            obj = default_values\n",
    "        elif not isinstance(obj, (list, tuple)):\n",
    "            obj = [obj]\n",
    "        return obj\n",
    "\n",
    "    labels = _make_list(labels)\n",
    "    colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])\n",
    "    for i, bbox in enumerate(bboxes):\n",
    "        color = colors[i % len(colors)]\n",
    "        rect = d2l.bbox_to_rect(bbox.detach().numpy(), color)\n",
    "        axes.add_patch(rect)\n",
    "        if labels and len(labels) > i:\n",
    "            text_color = 'k' if color == 'w' else 'w'\n",
    "            axes.text(rect.xy[0], rect.xy[1], labels[i],\n",
    "                      va='center', ha='center', fontsize=9, color=text_color,\n",
    "                      bbox=dict(facecolor=color, lw=0))\n",
    "\n",
    "d2l.set_figsize()\n",
    "bbox_scale = torch.tensor((w, h, w, h))\n",
    "fig = d2l.plt.imshow(img)\n",
    "show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,\n",
    "            ['s=0.75, r=1', 's=0.5, r=1', 's=0.25, r=1', 's=0.75, r=2',\n",
    "             's=0.75, r=0.5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990d9ccd",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "交并比（IoU）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea10897d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.395503Z",
     "iopub.status.busy": "2022-07-31T22:40:47.395278Z",
     "iopub.status.idle": "2022-07-31T22:40:47.401516Z",
     "shell.execute_reply": "2022-07-31T22:40:47.400895Z"
    },
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def box_iou(boxes1, boxes2):\n",
    "    \"\"\"计算两个锚框或边界框列表中成对的交并比\"\"\"\n",
    "    box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) *\n",
    "                              (boxes[:, 3] - boxes[:, 1]))\n",
    "    areas1 = box_area(boxes1)\n",
    "    areas2 = box_area(boxes2)\n",
    "    inter_upperlefts = torch.max(boxes1[:, None, :2], boxes2[:, :2])\n",
    "    inter_lowerrights = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])\n",
    "    inters = (inter_lowerrights - inter_upperlefts).clamp(min=0)\n",
    "    inter_areas = inters[:, :, 0] * inters[:, :, 1]\n",
    "    union_areas = areas1[:, None] + areas2 - inter_areas\n",
    "    return inter_areas / union_areas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36b02c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将真实边界框分配给锚框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00507028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.405738Z",
     "iopub.status.busy": "2022-07-31T22:40:47.405523Z",
     "iopub.status.idle": "2022-07-31T22:40:47.412165Z",
     "shell.execute_reply": "2022-07-31T22:40:47.411572Z"
    },
    "origin_pos": 20,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def assign_anchor_to_bbox(ground_truth, anchors, device, iou_threshold=0.5):\n",
    "    \"\"\"将最接近的真实边界框分配给锚框\"\"\"\n",
    "    num_anchors, num_gt_boxes = anchors.shape[0], ground_truth.shape[0]\n",
    "    jaccard = box_iou(anchors, ground_truth)\n",
    "    anchors_bbox_map = torch.full((num_anchors,), -1, dtype=torch.long,\n",
    "                                  device=device)\n",
    "    max_ious, indices = torch.max(jaccard, dim=1)\n",
    "    anc_i = torch.nonzero(max_ious >= iou_threshold).reshape(-1)\n",
    "    box_j = indices[max_ious >= iou_threshold]\n",
    "    anchors_bbox_map[anc_i] = box_j\n",
    "    col_discard = torch.full((num_anchors,), -1)\n",
    "    row_discard = torch.full((num_gt_boxes,), -1)\n",
    "    for _ in range(num_gt_boxes):\n",
    "        max_idx = torch.argmax(jaccard)\n",
    "        box_idx = (max_idx % num_gt_boxes).long()\n",
    "        anc_idx = (max_idx / num_gt_boxes).long()\n",
    "        anchors_bbox_map[anc_idx] = box_idx\n",
    "        jaccard[:, box_idx] = col_discard\n",
    "        jaccard[anc_idx, :] = row_discard\n",
    "    return anchors_bbox_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f39aab",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "给定框$A$和$B$，中心坐标分别为$(x_a, y_a)$和$(x_b, y_b)$，宽度分别为$w_a$和$w_b$，高度分别为$h_a$和$h_b$。\n",
    "我们可以将$A$的偏移量标记为：\n",
    "\n",
    "$$\\left( \\frac{ \\frac{x_b - x_a}{w_a} - \\mu_x }{\\sigma_x},\n",
    "\\frac{ \\frac{y_b - y_a}{h_a} - \\mu_y }{\\sigma_y},\n",
    "\\frac{ \\log \\frac{w_b}{w_a} - \\mu_w }{\\sigma_w},\n",
    "\\frac{ \\log \\frac{h_b}{h_a} - \\mu_h }{\\sigma_h}\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eebfb8d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.416428Z",
     "iopub.status.busy": "2022-07-31T22:40:47.416215Z",
     "iopub.status.idle": "2022-07-31T22:40:47.421036Z",
     "shell.execute_reply": "2022-07-31T22:40:47.420399Z"
    },
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def offset_boxes(anchors, assigned_bb, eps=1e-6):\n",
    "    \"\"\"对锚框偏移量的转换\"\"\"\n",
    "    c_anc = d2l.box_corner_to_center(anchors)\n",
    "    c_assigned_bb = d2l.box_corner_to_center(assigned_bb)\n",
    "    offset_xy = 10 * (c_assigned_bb[:, :2] - c_anc[:, :2]) / c_anc[:, 2:]\n",
    "    offset_wh = 5 * torch.log(eps + c_assigned_bb[:, 2:] / c_anc[:, 2:])\n",
    "    offset = torch.cat([offset_xy, offset_wh], axis=1)\n",
    "    return offset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b313211",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "标记锚框的类别和偏移量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "030efc01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.425243Z",
     "iopub.status.busy": "2022-07-31T22:40:47.425027Z",
     "iopub.status.idle": "2022-07-31T22:40:47.433080Z",
     "shell.execute_reply": "2022-07-31T22:40:47.432472Z"
    },
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def multibox_target(anchors, labels):\n",
    "    \"\"\"使用真实边界框标记锚框\"\"\"\n",
    "    batch_size, anchors = labels.shape[0], anchors.squeeze(0)\n",
    "    batch_offset, batch_mask, batch_class_labels = [], [], []\n",
    "    device, num_anchors = anchors.device, anchors.shape[0]\n",
    "    for i in range(batch_size):\n",
    "        label = labels[i, :, :]\n",
    "        anchors_bbox_map = assign_anchor_to_bbox(\n",
    "            label[:, 1:], anchors, device)\n",
    "        bbox_mask = ((anchors_bbox_map >= 0).float().unsqueeze(-1)).repeat(\n",
    "            1, 4)\n",
    "        class_labels = torch.zeros(num_anchors, dtype=torch.long,\n",
    "                                   device=device)\n",
    "        assigned_bb = torch.zeros((num_anchors, 4), dtype=torch.float32,\n",
    "                                  device=device)\n",
    "        indices_true = torch.nonzero(anchors_bbox_map >= 0)\n",
    "        bb_idx = anchors_bbox_map[indices_true]\n",
    "        class_labels[indices_true] = label[bb_idx, 0].long() + 1\n",
    "        assigned_bb[indices_true] = label[bb_idx, 1:]\n",
    "        offset = offset_boxes(anchors, assigned_bb) * bbox_mask\n",
    "        batch_offset.append(offset.reshape(-1))\n",
    "        batch_mask.append(bbox_mask.reshape(-1))\n",
    "        batch_class_labels.append(class_labels)\n",
    "    bbox_offset = torch.stack(batch_offset)\n",
    "    bbox_mask = torch.stack(batch_mask)\n",
    "    class_labels = torch.stack(batch_class_labels)\n",
    "    return (bbox_offset, bbox_mask, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c799fd7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "在图像中绘制这些真实边界框和锚框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a31352a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.437225Z",
     "iopub.status.busy": "2022-07-31T22:40:47.437012Z",
     "iopub.status.idle": "2022-07-31T22:40:47.687749Z",
     "shell.execute_reply": "2022-07-31T22:40:47.687041Z"
    },
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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    "ground_truth = torch.tensor([[0, 0.1, 0.08, 0.52, 0.92],\n",
    "                         [1, 0.55, 0.2, 0.9, 0.88]])\n",
    "anchors = torch.tensor([[0, 0.1, 0.2, 0.3], [0.15, 0.2, 0.4, 0.4],\n",
    "                    [0.63, 0.05, 0.88, 0.98], [0.66, 0.45, 0.8, 0.8],\n",
    "                    [0.57, 0.3, 0.92, 0.9]])\n",
    "\n",
    "fig = d2l.plt.imshow(img)\n",
    "show_bboxes(fig.axes, ground_truth[:, 1:] * bbox_scale, ['dog', 'cat'], 'k')\n",
    "show_bboxes(fig.axes, anchors * bbox_scale, ['0', '1', '2', '3', '4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0af64d0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "根据狗和猫的真实边界框，标注这些锚框的分类和偏移量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44676ba5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.699311Z",
     "iopub.status.busy": "2022-07-31T22:40:47.698762Z",
     "iopub.status.idle": "2022-07-31T22:40:47.703830Z",
     "shell.execute_reply": "2022-07-31T22:40:47.703224Z"
    },
    "origin_pos": 32,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 1, 2, 0, 2]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = multibox_target(anchors.unsqueeze(dim=0),\n",
    "                         ground_truth.unsqueeze(dim=0))\n",
    "\n",
    "labels[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "159bfafc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.706777Z",
     "iopub.status.busy": "2022-07-31T22:40:47.706257Z",
     "iopub.status.idle": "2022-07-31T22:40:47.711162Z",
     "shell.execute_reply": "2022-07-31T22:40:47.710544Z"
    },
    "origin_pos": 34,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1.,\n",
       "         1., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0df1fc1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.714081Z",
     "iopub.status.busy": "2022-07-31T22:40:47.713630Z",
     "iopub.status.idle": "2022-07-31T22:40:47.718592Z",
     "shell.execute_reply": "2022-07-31T22:40:47.717898Z"
    },
    "origin_pos": 36,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.00e+00, -0.00e+00, -0.00e+00, -0.00e+00,  1.40e+00,  1.00e+01,\n",
       "          2.59e+00,  7.18e+00, -1.20e+00,  2.69e-01,  1.68e+00, -1.57e+00,\n",
       "         -0.00e+00, -0.00e+00, -0.00e+00, -0.00e+00, -5.71e-01, -1.00e+00,\n",
       "          4.17e-06,  6.26e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d7e714",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "应用逆偏移变换来返回预测的边界框坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d1fe215",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.722190Z",
     "iopub.status.busy": "2022-07-31T22:40:47.721619Z",
     "iopub.status.idle": "2022-07-31T22:40:47.726681Z",
     "shell.execute_reply": "2022-07-31T22:40:47.726069Z"
    },
    "origin_pos": 38,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def offset_inverse(anchors, offset_preds):\n",
    "    \"\"\"根据带有预测偏移量的锚框来预测边界框\"\"\"\n",
    "    anc = d2l.box_corner_to_center(anchors)\n",
    "    pred_bbox_xy = (offset_preds[:, :2] * anc[:, 2:] / 10) + anc[:, :2]\n",
    "    pred_bbox_wh = torch.exp(offset_preds[:, 2:] / 5) * anc[:, 2:]\n",
    "    pred_bbox = torch.cat((pred_bbox_xy, pred_bbox_wh), axis=1)\n",
    "    predicted_bbox = d2l.box_center_to_corner(pred_bbox)\n",
    "    return predicted_bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8caf7826",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "以下`nms`函数按降序对置信度进行排序并返回其索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "672f4836",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.729658Z",
     "iopub.status.busy": "2022-07-31T22:40:47.729228Z",
     "iopub.status.idle": "2022-07-31T22:40:47.734696Z",
     "shell.execute_reply": "2022-07-31T22:40:47.734094Z"
    },
    "origin_pos": 41,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def nms(boxes, scores, iou_threshold):\n",
    "    \"\"\"对预测边界框的置信度进行排序\"\"\"\n",
    "    B = torch.argsort(scores, dim=-1, descending=True)\n",
    "    keep = []\n",
    "    while B.numel() > 0:\n",
    "        i = B[0]\n",
    "        keep.append(i)\n",
    "        if B.numel() == 1: break\n",
    "        iou = box_iou(boxes[i, :].reshape(-1, 4),\n",
    "                      boxes[B[1:], :].reshape(-1, 4)).reshape(-1)\n",
    "        inds = torch.nonzero(iou <= iou_threshold).reshape(-1)\n",
    "        B = B[inds + 1]\n",
    "    return torch.tensor(keep, device=boxes.device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01cda819",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将非极大值抑制应用于预测边界框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88353c09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.737579Z",
     "iopub.status.busy": "2022-07-31T22:40:47.737150Z",
     "iopub.status.idle": "2022-07-31T22:40:47.745145Z",
     "shell.execute_reply": "2022-07-31T22:40:47.744534Z"
    },
    "origin_pos": 44,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold=0.5,\n",
    "                       pos_threshold=0.009999999):\n",
    "    \"\"\"使用非极大值抑制来预测边界框\"\"\"\n",
    "    device, batch_size = cls_probs.device, cls_probs.shape[0]\n",
    "    anchors = anchors.squeeze(0)\n",
    "    num_classes, num_anchors = cls_probs.shape[1], cls_probs.shape[2]\n",
    "    out = []\n",
    "    for i in range(batch_size):\n",
    "        cls_prob, offset_pred = cls_probs[i], offset_preds[i].reshape(-1, 4)\n",
    "        conf, class_id = torch.max(cls_prob[1:], 0)\n",
    "        predicted_bb = offset_inverse(anchors, offset_pred)\n",
    "        keep = nms(predicted_bb, conf, nms_threshold)\n",
    "\n",
    "        all_idx = torch.arange(num_anchors, dtype=torch.long, device=device)\n",
    "        combined = torch.cat((keep, all_idx))\n",
    "        uniques, counts = combined.unique(return_counts=True)\n",
    "        non_keep = uniques[counts == 1]\n",
    "        all_id_sorted = torch.cat((keep, non_keep))\n",
    "        class_id[non_keep] = -1\n",
    "        class_id = class_id[all_id_sorted]\n",
    "        conf, predicted_bb = conf[all_id_sorted], predicted_bb[all_id_sorted]\n",
    "        below_min_idx = (conf < pos_threshold)\n",
    "        class_id[below_min_idx] = -1\n",
    "        conf[below_min_idx] = 1 - conf[below_min_idx]\n",
    "        pred_info = torch.cat((class_id.unsqueeze(1),\n",
    "                               conf.unsqueeze(1),\n",
    "                               predicted_bb), dim=1)\n",
    "        out.append(pred_info)\n",
    "    return torch.stack(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28266465",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将上述算法应用到一个带有四个锚框的具体示例中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a96a357d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.748207Z",
     "iopub.status.busy": "2022-07-31T22:40:47.747662Z",
     "iopub.status.idle": "2022-07-31T22:40:47.752500Z",
     "shell.execute_reply": "2022-07-31T22:40:47.751890Z"
    },
    "origin_pos": 46,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "anchors = torch.tensor([[0.1, 0.08, 0.52, 0.92], [0.08, 0.2, 0.56, 0.95],\n",
    "                      [0.15, 0.3, 0.62, 0.91], [0.55, 0.2, 0.9, 0.88]])\n",
    "offset_preds = torch.tensor([0] * anchors.numel())\n",
    "cls_probs = torch.tensor([[0] * 4,\n",
    "                      [0.9, 0.8, 0.7, 0.1],\n",
    "                      [0.1, 0.2, 0.3, 0.9]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15c46aa6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "在图像上绘制这些预测边界框和置信度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00019613",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.755342Z",
     "iopub.status.busy": "2022-07-31T22:40:47.754913Z",
     "iopub.status.idle": "2022-07-31T22:40:47.986509Z",
     "shell.execute_reply": "2022-07-31T22:40:47.985858Z"
    },
    "origin_pos": 48,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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      ],
      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = d2l.plt.imshow(img)\n",
    "show_bboxes(fig.axes, anchors * bbox_scale,\n",
    "            ['dog=0.9', 'dog=0.8', 'dog=0.7', 'cat=0.9'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecaf8c30",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "返回结果的形状是（批量大小，锚框的数量，6）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f39ce65e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:47.989839Z",
     "iopub.status.busy": "2022-07-31T22:40:47.989374Z",
     "iopub.status.idle": "2022-07-31T22:40:47.999030Z",
     "shell.execute_reply": "2022-07-31T22:40:47.998421Z"
    },
    "origin_pos": 51,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.00,  0.90,  0.10,  0.08,  0.52,  0.92],\n",
       "         [ 1.00,  0.90,  0.55,  0.20,  0.90,  0.88],\n",
       "         [-1.00,  0.80,  0.08,  0.20,  0.56,  0.95],\n",
       "         [-1.00,  0.70,  0.15,  0.30,  0.62,  0.91]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = multibox_detection(cls_probs.unsqueeze(dim=0),\n",
    "                            offset_preds.unsqueeze(dim=0),\n",
    "                            anchors.unsqueeze(dim=0),\n",
    "                            nms_threshold=0.5)\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795e1cf9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "输出由非极大值抑制保存的最终预测边界框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8447f4ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T22:40:48.002159Z",
     "iopub.status.busy": "2022-07-31T22:40:48.001602Z",
     "iopub.status.idle": "2022-07-31T22:40:48.218922Z",
     "shell.execute_reply": "2022-07-31T22:40:48.218285Z"
    },
    "origin_pos": 53,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
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    "fig = d2l.plt.imshow(img)\n",
    "for i in output[0].detach().numpy():\n",
    "    if i[0] == -1:\n",
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